Commit 9b187c0b authored by Heller, William T.'s avatar Heller, William T.
Browse files

Merge branch 'run_time_issue' into 'master' 

Run time issue

See merge request !16
parents f30bde74 6648e75c

sas_temper/sas_temper_engine.py

+57 −42
Original line number Diff line number Diff line
@@ -12,6 +12,7 @@ Oak Ridge National Laboratory, 2020 
import numpy as np

import math as m

import copy

import time



import sas_temper.modelconfig as modelconfig

import sas_temper.sas_temper_config as config
@@ -23,33 +24,42 @@ import sas_temper.sas_calc as sas_calc 
# modconf is the model to be used and the parameter ranges

# d is the input data to be fit against

def sa_control(fconf, modconf, d):

    res = np.empty(fconf.models, "object")            # the parameters returned from the fitting

    mprof = np.empty(fconf.models, "object")        # the profiles returned from the fitting

    mprof_usm = np.empty(fconf.models, "object")    # the unsmeared profiles returned from the fitting - may be empty

    # this is the number of refinements to do and how many models to use - yes, they are 'magic numbers'

    refines = 5

    refine_models = 10

    

    # set  up the memory

    res = np.empty(refine_models, "object")            # the parameters returned from the fitting

    mprof = np.empty(refine_models, "object")        # the profiles returned from the fitting

    mprof_usm = np.empty(refine_models, "object")    # the unsmeared profiles returned from the fitting - may be empty

    

    localconf = modelconfig.ModelConfig(modconf.name,modconf.category,modconf.params,modconf.sq)

    

    # this is the number of refinements to do - yes, it is a 'magic number'

    refines = 5

    # st_time = time.time()

    

    # the number of refinement iterations to do

    for j in range(0,refines) :

        for i in range(0,fconf.models):

        for i in range(0,refine_models):

            if j is 0:

                res[i],mprof[i],mprof_usm[i] = sa_engine(fconf,modconf,d)

            else:

                res[i],mprof[i],mprof_usm[i] = sa_engine(fconf,localconf,d)

            

        # refine the ranges to start over

        localconf = sa_refine(fconf.models, res)

        localconf = sa_refine(refine_models, res)

    

    best = 1000000000.00

    hit = 0

    for k in range(0, fconf.models):

    for k in range(0, refine_models):

        if res[k].chisq < best:

            hit = k

            best = res[k].chisq

    

    #end_time = time.time()

    #dif_time = end_time-st_time

    #junk = "Time for a single model = " + str(dif_time)

    #print(junk)

    

    return res[hit], mprof[hit], mprof_usm[hit]
@@ -90,9 +100,11 @@ def sa_engine(fconf, modconf, d): 
    temp = 10.0

    r = 1.0

    schedule = 1

    iters = 1

    hit = False

    while schedule <= fconf.temperatures:

        iters = 1

        

        while iters <= fconf.iterations:

            #define the model to test

            f = define_model(schedule,modconf,temp,r,cur)
@@ -127,13 +139,16 @@ def sa_engine(fconf, modconf, d): 
            if hit:

                cur = copy.deepcopy(f)

                

        iters += 1

            iters = iters + 1

            

            #noise = "schedule " + str(schedule) + "; iteration " + str(iters)

            #print(noise)

        

        # we drop the temperature, tighten the range and increase schedule

        if iters % fconf.iterations :

        temp = temp*fconf.temp_rate

        r = r*fconf.param_rate

            schedule += 1

        schedule = schedule + 1

        

    

    # as the final step, we estimate the uncertainties with the jacobian

    f = copy.deepcopy(fbest)